{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "version": "3.7.3-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython2",
  "version": 2,
  "kernelspec": {
   "name": "python271664bitenvvirtualenv8836d05012704695b9eba33d763507b5",
   "display_name": "Python 2.7.16 64-bit ('env': virtualenv)"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Re-execution rate per subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import rc\n",
    "import pandas as pd\n",
    "\n",
    "# plot style\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_style({'font.family': 'Times New Roman'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = pd.read_csv(\"data/total.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "subject\nSocial Sciences                                                                              2943\nEarth and Environmental Sciences                                                               63\nMedicine; Health and Life Sciences                                                             63\nBusiness and Management                                                                        59\nLaw                                                                                            52\nEarth and Environmental Sciences;Mathematical Sciences;Medicine; Health and Life Sciences      44\nArts and Humanities                                                                            36\nComputer and Information Science                                                               30\nOther                                                                                          28\nAgricultural Sciences                                                                          19\ndtype: int64"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['subject'] = total.subject.str.replace(';Social Sciences', '')\n",
    "total.groupby(['subject']).size().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "total.subject = total.subject.apply(lambda x: 'Physics' if 'Physics' in x else x)\n",
    "total.subject = total.subject.apply(lambda x: 'Health and\\nLife Sciences' if 'Health and Life Sciences' in x else x)\n",
    "total.subject = total.subject.apply(lambda x: 'Earth and\\nEnvironmental Sciences' if 'Earth and Environmental Sciences' in x else x)\n",
    "total.subject = total.subject.apply(lambda x: 'Law' if 'Law' in x else x)\n",
    "total.subject = total.subject.apply(lambda x: 'Computer and\\nInformation Science' if 'Computer and Information Science' in x else x)\n",
    "total.subject = total.subject.apply(lambda x: 'Agricultural Sciences' if 'Agricultural Sciences' in x else x)\n",
    "total.subject = total.subject.apply(lambda x: 'Business and\\nManagement' if 'Business and Management' in x else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "subject\nAgricultural Sciences                  21\nArts and Humanities                    36\nBusiness and\\nManagement               60\nComputer and\\nInformation Science      45\nEarth and\\nEnvironmental Sciences      77\nHealth and\\nLife Sciences             133\nLaw                                    65\nMathematical Sciences                  14\nOther                                  28\nPhysics                                19\nSocial Sciences                      2943\ndtype: int64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.groupby(['subject']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>success</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>subject</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Agricultural Sciences</th>\n      <td>18</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Arts and Humanities</th>\n      <td>31</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>Business and\\nManagement</th>\n      <td>35</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>Computer and\\nInformation Science</th>\n      <td>25</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>Earth and\\nEnvironmental Sciences</th>\n      <td>48</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>Health and\\nLife Sciences</th>\n      <td>54</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>Law</th>\n      <td>31</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>Mathematical Sciences</th>\n      <td>10</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Other</th>\n      <td>13</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>Physics</th>\n      <td>18</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Social Sciences</th>\n      <td>1759</td>\n      <td>1184</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "success                               0     1\nsubject                                      \nAgricultural Sciences                18     3\nArts and Humanities                  31     5\nBusiness and\\nManagement             35    25\nComputer and\\nInformation Science    25    20\nEarth and\\nEnvironmental Sciences    48    29\nHealth and\\nLife Sciences            54    79\nLaw                                  31    34\nMathematical Sciences                10     4\nOther                                13    15\nPhysics                              18     1\nSocial Sciences                    1759  1184"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = total.groupby(\n",
    "    ['success', 'subject']).size().reset_index().pivot(columns='success', index='subject', values=0)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>success</th>\n      <th>0</th>\n      <th>1</th>\n      <th>total</th>\n      <th>s</th>\n      <th>f</th>\n    </tr>\n    <tr>\n      <th>subject</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Health and\\nLife Sciences</th>\n      <td>54</td>\n      <td>79</td>\n      <td>133</td>\n      <td>59.398496</td>\n      <td>40.601504</td>\n    </tr>\n    <tr>\n      <th>Other</th>\n      <td>13</td>\n      <td>15</td>\n      <td>28</td>\n      <td>53.571429</td>\n      <td>46.428571</td>\n    </tr>\n    <tr>\n      <th>Law</th>\n      <td>31</td>\n      <td>34</td>\n      <td>65</td>\n      <td>52.307692</td>\n      <td>47.692308</td>\n    </tr>\n    <tr>\n      <th>Computer and\\nInformation Science</th>\n      <td>25</td>\n      <td>20</td>\n      <td>45</td>\n      <td>44.444444</td>\n      <td>55.555556</td>\n    </tr>\n    <tr>\n      <th>Business and\\nManagement</th>\n      <td>35</td>\n      <td>25</td>\n      <td>60</td>\n      <td>41.666667</td>\n      <td>58.333333</td>\n    </tr>\n    <tr>\n      <th>Social Sciences</th>\n      <td>1759</td>\n      <td>1184</td>\n      <td>2943</td>\n      <td>40.231057</td>\n      <td>59.768943</td>\n    </tr>\n    <tr>\n      <th>Earth and\\nEnvironmental Sciences</th>\n      <td>48</td>\n      <td>29</td>\n      <td>77</td>\n      <td>37.662338</td>\n      <td>62.337662</td>\n    </tr>\n    <tr>\n      <th>Mathematical Sciences</th>\n      <td>10</td>\n      <td>4</td>\n      <td>14</td>\n      <td>28.571429</td>\n      <td>71.428571</td>\n    </tr>\n    <tr>\n      <th>Agricultural Sciences</th>\n      <td>18</td>\n      <td>3</td>\n      <td>21</td>\n      <td>14.285714</td>\n      <td>85.714286</td>\n    </tr>\n    <tr>\n      <th>Arts and Humanities</th>\n      <td>31</td>\n      <td>5</td>\n      <td>36</td>\n      <td>13.888889</td>\n      <td>86.111111</td>\n    </tr>\n    <tr>\n      <th>Physics</th>\n      <td>18</td>\n      <td>1</td>\n      <td>19</td>\n      <td>5.263158</td>\n      <td>94.736842</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "success                               0     1  total          s          f\nsubject                                                                   \nHealth and\\nLife Sciences            54    79    133  59.398496  40.601504\nOther                                13    15     28  53.571429  46.428571\nLaw                                  31    34     65  52.307692  47.692308\nComputer and\\nInformation Science    25    20     45  44.444444  55.555556\nBusiness and\\nManagement             35    25     60  41.666667  58.333333\nSocial Sciences                    1759  1184   2943  40.231057  59.768943\nEarth and\\nEnvironmental Sciences    48    29     77  37.662338  62.337662\nMathematical Sciences                10     4     14  28.571429  71.428571\nAgricultural Sciences                18     3     21  14.285714  85.714286\nArts and Humanities                  31     5     36  13.888889  86.111111\nPhysics                              18     1     19   5.263158  94.736842"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#temp.fillna(0.0, inplace=True)\n",
    "temp['total'] = [i+j for i,j in zip(temp[1],temp[0])]\n",
    "#temp['total'] = [i+j for i,j in zip(temp[2],temp['total'])]\n",
    "\n",
    "temp['s'] = [(i*1./j) * 100 for i,j in zip(temp[1], temp['total'])]\n",
    "temp['f'] = [(i*1./j) * 100 for i,j in zip(temp[0], temp['total'])]\n",
    "\n",
    "#temp['sortby'] = [i+j for i,j in zip(temp['s'], temp['t'])]\n",
    "\n",
    "temp = temp.sort_values(by=['s'], ascending=False).head(11)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = list(temp.index)\n",
    "r= np.arange(len(temp['s']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers import autolabel, autolabel_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ykzTLzDlz5lBeXk7fvn3ZsWMH3bt3p3HjxkyZMoULL7wQMOEdzZs3Jz8/n3bt2tG0aVNmzJhRpfQsFkvqkjTKLFzw6pAhQ6oejxw5EoDOnTtXvXb22WcntF8Wi6V+kDTKLFG4hoUmTZqQlZXFli1buO6666re//7772nfvj27du2yhgWLpR5z0Csz17AwcOBAmjdvzvLly6vemzJlCnv37qW4uJgTTjiBffv20bZtW+bPn0+TJk3YsGFDxCWsqyhbtGjB9u3b2bJlC7/85S8BWL9+PTNmzCAzM5MjjjjCKkqLJU6SQpkVF2/h5ptvrqVVJRA5sj8zc/9cTyLCkUceyfjx4zn99NNZunRp1dJ1xYoVjBgxgtdff50TTjiB3NxcFi5cSLNmzSgtLWXatGlcdtllYc/nKsr+/fvTu3dvPvjgg6r3OnTowDHHHENxcTE5OTkxK0qLxVKTpFBm7bJb8O9X7oxbznm3v7jfa65h4eijj2bJkiXk5uYCZlY2ePBgvvnmGzp27OjJsBCoKFu1alWVG8qldevWTJkyhYEDB8asKC0WS02SQpklkkDDQo8ePaoeu7MzEal6LVbDgqsoy8rKGD9+PIcccggXX3wxs2fPJjs7m71791YtJ60F1mKJj4NemSUSV1EGF3gItMC6WAusxRIfSeM0a7FYLJE4qGdmiTQsWCyWA4snZSYidwHnAp8A5cBGoK2q/tXHviWcRBoWLBbLgcVL3czWgGBKzDUCfq2qvxSRp0Sks6rm+93JZCTRs7758+fzww8/MGjQIMaOHcuoUaOq3rMZRCwHI15mZo2BBcAE4DNgnfP6eqAfYJUZiZ31rV27loYNG1JWVsauXbtIT6/e+gzOIGL91ywHC17rZv5FRD4HJgGvBMiK2qBg62Z6k11WVsaMGTMoLS1lwoQJNGzYkPz8fNavX0+HDh2YOXMmlZWVNTKIhPJfC9dnd8aXkZFBr169qlxT5s6dy6JFiygoKOCSSy5h/fr1YWd8qTLOiZCbSNn1vc+h5PpJPAaAn4B3AdenoBMwNtqDbd1Mb7IzMzO54oor2LBhA40aNeLSSy+lsLCQDh06xJRBJFSfy8vLadiwIZWVlRxyyCFUVlZXEuzXrx/9+vVj6tSpdOzYkdLS0rAzvlQZ50TITaTs+t7nUHJrI5a6mV72zG4EemCWms8A14jIJcASVV0TqzyLN9q3b18VIeBmC4k3g0hJSQlz585l1apVHH/88fu9v2nTJtq2bUuLFi08RSy4s742bdqwe/duzj33XACWLFnCrFmzSEtLY+DAgXafz+IJL8vMt4JeSioLpiU8bdu25cwzz2TatGls376d4uJiwIR2DRkyhLy8PE455RQg9ogFd5+vtLQUEWHhwoX8+OOPdO3aFRGpil21+3wWrxzUfmaW/Qmc8bm4M75BgwZVvRZrxIK7z/ePf/yDU089lfT09KryaZmZmaxcuZKzzjrL06xv9erVTJ06lWbNmlFSUsL69esZMWIEbdu2jWmvz5LcWGWWZCSro6+7z5eRkYGqsm/fPg499FCmTJkCmAwlzZo14/LLL6egoCCmWV+XLl3YuHEjrVu3pkePHkydOpW2bdsCse31WZIbq8ySjGR29G3fvj1XXXVVjdfcWV/gfp+XONVOnTrx1Vdfcdhhh+1XMDfevT5LcmBjMy1Jz/fff8/69evJzc1lwYIF9OnTB6Bq1peXl1dlRXP3+jp37szGjRvtrCyFsDMzSxXJuoQNzkri4sdenyV5sMrMUkWilrDJqiQtyYVVZpaEk8z7fJbkwSozS1JjZ30WF6vMLElNImd933//PQsWLKBFixZkZ2ezY8cOLrroohrv2zKE9QdrzbRYwtCvXz9GjBhBRUUF27Ztq1EjYsqUKRQXF5OXl0dOTg7Z2dl0796d+fPnk5GRwYYNG+qw5wcnVplZLGHIzMxkyZIlnHDCCfTp04dZs2ZRWloKGCffs846i/x8k/EqNzeXoqIimjVrRnl5OdOmTavLrh+U2GWmxRKGTz/9lK1bt9KoUSPat29Phw4dqKys9KUMocV/rDKzWMJw8cUXh3zdjzKEFv+xy0yLxZISWGVmsVhSAqvMLBZLSmD3zCyWEFhn3OTDS9rsPsBIIBe4E9gBPAdsB65U1Qpfe2ix1AE2BCv58DIz26Oqt4jIIOBWoAgYrarf+ds1i8ViiR4vNQDyAo79FtgK3CMiC1T1iWjl2FJz3mTbPts+h5Nd3/tcL0vNiUgacBLwtKqWAeNE5MNYZNhSc95k2z7bPoeTXd/7nOhSc16tmVcDLwUd/5VHWRaLxRI3XgwAo4ETgHOBUhE5BPgImO5v1yyW1KW0tJQ///nP3HvvvWzYsIGxY8cyatQoANavX8+MGTPIzMzkiCOOiCkjhyv3pptuYsaMGWzZsoXrrrsOgEWLFrF7924KCgro2bNnymX68LJnNjoB/bBYDioaNGhA48aN2bZtG7t27SI9vXqR06FDB4455hiKi4tjriPqym3VqhV9+/Zl+fLlVe/NmDGDG264gaVLl9KmTZuUq09qnWYtljpk1qxZLFy4kPz8fNavX1/1euvWratyqXnNyHHIIYewdOnSqufnnXce06dPr6pWlWqZPqzTrMVSB5SVlbFt2zYGDBhAixYtKCgooEOHDkyZMoXs7Gz27t1bteyLJSOHK3f58uVs2bKF3NxcwORf69OnD4cccghXXnllzHKTAavMLJY6IDMzkwcffLDq+ciRI4Ga9UNdYsnIESzXxZUbWMkq1TJ92GWmxWJJCawys1gsKYFdZlosB5hEBbEf7MHxVplZLAeYRAWxH4jg+D179jB27FhycnI444wzyMvLY9KkSZx++umsWbOGffv20b9/fwD27t1bY18u0VhlZrFYoiY/P5+SkpIqQ8KRRx7J2rVryc7OpmHDhpSUlNCoUSOWLFlCVlYWOTk5NGjQ4ID0ze6ZWSyWqBERRo4cSV6eyTeRlpZG165dmTZtGl27diU3N5cVK1ZQVFREmzZtmDVr1gHrm52ZWSyWqJkzZw7l5eUcffTR5OXlsWnTJpo2bcrRRx8NwIIFC8jNzSUrK4u8vDyOOuqoA9Y3q8wsFkvUuPthLsHKynXSPeaYYzjmmGMOWL8gCZeZa34qiqrdTxuLfZW/a/detpbs8iQz0bKTpc/RfneBrNsQ+/eYbOOcLN9fIIGhV4Hs2bOHHTt2eJYbD0mlzJatXEdR8Q4e+dM4Nm4uYceuPXz4r2958Ll/APDFtAXc8ts32L13H4Ubt7Ju/ZaY5Rdu2soTr4znyVfHs2XrDt4cN4W3/zkVgOlzlfJyb1nBl61cx5ZtO5n4zSJ0VSEAY/8zkydfHR+XbFfu8h8LeeSP43yTG6rPe/aW8o/PZ/Lep9+Qnp4ek+zgsd20pYT3x3/Di+9NZPvO3ZSXV3D3E++xcXMJcxb/yAPPfsjcJatpktWQabPyaj9BwHk+mzSPJ18dz83/9xoF67fwuz//k3ueeJ8lKwriGufAaw+gqHg7dz76Nk2yGsUlN3BcACZ8NZfPJs2NS25gn1/5+5e8MXYyADPn/8DCvDUxf3+BrF69mm3btvH6669TXFxMcXExX3zxBR999BEVFRUsWLCA8vJyT32Oh6RaZn4ycTYP3n4Rk2cuAaBZk8acdkIvSsvMwJ06sCerCzbRuGED+vXuwhOvjOehO0IXcg0n/75bzuf43B48/9Z/aNSwAcXbdnJC3yPIatyQ9m1a8N9vFvKzU/t46nvP7jns3lvKpi0ldD+8HXkrf6JVi6Y0yWrkWfYnE2dz/SWD2Fdaxt7SUsrKyn2RG6rPhZuKyWnXisrKCqbNWhaT7OCxnTn/B3LatSKnXStmzv+Bxo0a0rF9awD69urMitXr6dC2Ba2aN2Xq98sYPCC6vRf3GgH49H+z6dShNY/+chjjv5zNgGO7M3vxKs/jHHjt7dq9l9XrNtG0SSOAuL6/wHEBaNOqGZu2lMQlN7DPGRlpbNxcwpqfivhqxmI6tG1Jj87tY5Id6MO2Y8cOmjVrxo4dO5g+fTqlpaWkpQGkMX78eNLT03n55Zdp3LhxDRmJ9l9LqpnZrj1793utWZPGLFq+lrKycjIzM2jcqAFzlvxIWloaO3bt3742+Q0yM1i8fC1XXXAipWXlnNxfmLVwFUXF22nVvKmnZY8re37eGq46/0R0VSElO3bzyJ2X0i67BcUlOz3L3rVnL//9eiFffbuEvFWFlOz0R26oPm/asp2M9DTS09Np3KhBTLKDx3boCb3J/6mIL2csplmTxixYls+8Jav5dt5y0tLS6NWjIxO+NFlGd+3eF1OfwSxPO7ZvVf2GudviGudApny/jHlL88lbVciSFQVxyQ0cl2Di/f7AKMRv5ixn8YoChp7Ym5x2rZg0c2lMsttlt2Dex48w7+NHuOLc45j38SPcdsWpfPnmvcwe9zvuu+lsLh56LBP/ei/jX7qTe35+ZlV7969ddgtPnyNakmpmlt3yECorKykq3sHGLSXs2buP+cvWcHxudwDGffEdLZs3pcfhHSgvr6Bt60Nilv/xxFksyFtDi2ZZXHXBSSxZUcDhOdlUVsLm4h0c2aWD576fOrAnEybNpdthbSktK+dvE6bTvo2Zfaz4cb0n2dktD+Gm4aeSlpbGD/nrKS31R26oPp8/tC+fTJxNWXk5l51zPIt0bdSyg8f2tqtOp1ePjvTo3IET+x7BiX2PYO++Uk7seyQTJs2lRbMsTuh7BACtWzaNqc+VlZXMXryKC4f2A2DuktX069UF8P4dBl977mzmh/z19D6iE98vWOlZbuC43HXDOWwq3s7GLdvj6q8r+9t5K9i1Zx+DBxxF/95dGfufmTRvmsUZJx/NqjUbfRmL7oe1q/ouO3Vo7Xks4iVuZSYimcDdwGpgq6r+L16Z4bj2opNZkLeGJ399RdVrh+e0qXp81fknVT3+dt4Krr7wJGLh2otOZt2GYi49a0DVazdfNqRa5vwVXHCat2ycruyLTq+2Bl1z4clVjwuLtnqS7Y5Jn56defq+q3yTG67PV19QPaaxyA41tsfn9qjR5t6bzwOocb4NRds4pb/E1OcFeWtqyOjXu4unPoeSG3jtAVVjHo/c4HEJ7Lsf39+Jzo8CwC+uGFr1ePrc5b6NReB3GU+f48GPmdkVwI+q6hY1SZgya9u6OQ0bRNflo7rn0Kp59L/otcnfvXcfZ51yTI2MoPVBdrL0OZbvLpCGDTNr3IzxnKc+jnOyfH8HQm68pFVWVsYlQEReBN5T1e9E5FPgRlWNuBCfM2dOfCe1WCwHDf37948cGe/gx8ysEigNkFerSo62cxaLxRItfswFZwJdncf7VHWzDzItFoslJvxQZv8AOovItcBffJBnsVgsMRP3npnFYrHUB5LKadZisVjCYZWZxWJJCawys1gsKYFVZhaLJSWwysxisaQESRVoHgsikqaqB7WpVkQaA52BYmCzqoZNMiUig9i/Bll3VX0rgf3rBhQCJwMbVHVRAs/1K+B94A/AbFX9c5zyfL++EjUeB3Kc65KUm5mJyHMi0h2YKSKPxSHnjYDHDX3pnJF1gl+youA14BJgM/BULW2vAU4DRgNDnceDwzUWkTMCHl/vsX+3AQOAO4EhHmVESyEmjvh74AevQvy6vsKQqPFI2DiLSLrzv6eIHPhUGQGk4sxsKjASeARoFoecDOdiLQd6Alf60DcwDsaHAjuByapaWtsBcTAO2Oo8HlJL2ztVtUxEznQzn4S7WUXkEWC4iLg5mZcC73ro3zrgd8DPMTdcImkDHA88BDwBfO5Rjl/XVygSNR6JHOe3ROQvwBhgIvCkz/KjJuVmZkBjIB+YD/SvpW0kvgG+BKYAk+LvVhUznf+/xFwAieZOYDLwaqRGqlrmPOwiIi+JyFjMEjVU28eA01V1qKoOxczmvPA34AbM8vbfHmVEy1TgY6ACo9C84tf1FYpEjUcix3ka8ABwOxBfwYI4SUplJiIdI7ydibngMoH/xnGa3cCJwEpA45ATzGfA+cCjqvobH+WGYiZwLXAV8HY0B6jq65gl6f8D7o3Q9FERmSwik4HpHvv3NGamsBWzrE0kdwItVXUd3pUv+Hd9hSJR45GuDCJKAAAeAklEQVTIcZ4BPI7Zl93os+yYqPfKTESmisgkEVkUcPP8M8IhXYEFzkV7VxynduUUxCknmDOB+4FbRORPPsoNxR+BVsB64OFoDhCRl4H3gD8RWUmpqp6mqqdhPpMX/gVMVNUS4ByPMqLlK2CViHQBcuOQ49f1FYpEjUcix3kkRok1BuqmLJNDMuyZXaWqP4nIcFUdByAiN0ZovxHo4AS+t47jvH7JCWYCkIdRGF/5KDcUf1fVDSKSBZxNdDOS5ap6B4CIHB6h3WARcZVCDt5uksbAbSLyIAlM6umwHHPjNQN+EYecRF0XkLjxSOQ4T1HVfAAReRaz8qgT6r0yU9WfnIcdRWQgZiP3KiCcy8CHGMtcOhB9aabIci6JQ04wt6jqQgARaYWZnieKchH5BDgCeCbKYwaJyLHO40hK6iXMEhwg+rzWNfkn5uJvpaprPcqIlmzMXlkTjFHHK35dX6FIyHio6gciMh5IRHmktiJyK3As0CAB8qOm3i8zA3gbs5w5A/MLG46RGEvVv4Gj4zhfX4zibAs8H4ecYAaKyF9F5C1grI9yQzERY2g4D2PRioaXgEedvz9EaHcccAdQhner3t8wy+BiEbnHo4xoOQ/Y4cwi4tmr9Ov6CoWv4yEiY5z/n2CWmhMwe1y+oaqvAKswSvgaP2XHSjIps76Y/m4jsjVqm6qucS7aeJYTt2EsmlMwljC/yMJYF0cDH/goNxT/xmzOjibyZn4gmzH+ZadiFEA4SoB/O3tHN3ns31vODGQnxiiSSOYDFSIyAGPY8Ypf11cofB0PVXXvk985lufTgGHxygUQkfuc/y9hVkpXYKzFdUa9X2YGcAvwOiZN95oI7dJE5GzMtLdTrCcJ8IRfhDHj+00XYC5wK3AK8Ga8AkUkI4x3/zuq+oHT5rAoxd2MseQuJfISuBI4SUQG432cmonI+0Bv4COPMqJlJmasMzDXklfiur5qwffxEJHOQD8Rcf0Nh+GPL9jrzn93Dxi8bzf4QjIps4VEd9O8CVwHbAcu8nCeqzHWP6g2Y3cj/B5drDyMqZlQgQc/JWcTN/h7OxTj5xPMKBFxZw85RHexrcPMRA8DIpURfx04CzNb9rQMV9WxIvIZRsEk2ns8DfgC09/hmJmaF+K9vsKSoPEYgln+nYr5AVrmh1BVdZVjW8wPxa+BFX7I9koyKbNOVCuX7oRXLsOBEzAX7aWYGy4WRjme8H1VdR6AiMSzLKmBqrqOhd+KyD88iCjE7FEMw0zrK4FBYdoOwyjOvUC0dfcmAhuAdsB/IrS7EjgdM85348E9Q0TGYTbmwVgG43GZqI1nqR6veAwA8V5fYUnEeKjqOyIyTlV3OudoU9sxMdIQsyROw2wB1RnJpMx+5S6lalEuJ2H2iSoxM6qYcBTZ/cDVIuIWZynHuy8VACIiqhrsfLvPQ//ecuS1UdUpTmxcOFeV/4dRfr/HLB9rjThwFThm+RCJwYAb7nRkbXLD8LmqvgkxLYO9MoHqSI5I2xS1Edf1VQu+joeIPKmq/wf8W0QqMArnMKBH5CNjojXmR20k5gdjvI+yYyKZlNnbInI0Zh8nknLZTnWAdHfMZnvUiEga5kJtjwnPGItxII2XSSISaFF0L6z3PcrbJiL/wtxQL4dp8y9MtowSETkXf8OnvqI63Clk2FMUXOf4a6VhZiTH1tI+Hn6GUUTuuMd0XQQQ1/VVC76Oh6PIAK5zXT1q8R30wpeq+qzz+FKfZcdEMimzcZjqT4sxs4xwuFPdNLz5vdyDsdSdg1miudkG4koZAwxX1W8DX3Asa16ZjAmzSsf4kYUi0FlyYjRCReQ0YA5wGZCvql+GaXorxtpbiTFqvBGmXSTuwEQSVIiI19ldtNyJUfyrMd+vV+K9viKRqPE4VUSGYK6VTvi4NMYYgXIxzsT/VdVEGM2iIpmUWS+Mf8/jmIvyuTDt3Dqe/8VYDWNlraoG+n8tEZG4nSODFZnz2qw4RP4bY6ioxGwW76d0PDpL9sUowROpLu4cilcDIjIixcpGYgTGOvgsZj90uUc50TAGmKOq3zghW3d4lBPv9RWJRI3HSZgth0Qsjf+CWWo+jgnRG4dRage8fm7S+Jmp6pOqukBVR6lqJP+nn2F8gQqA/4vQLhyhxqSRBzmJ5h1V/bmq3kiQv1OczpIZmCwIjxEhnxlwjxMzOxnvWUVmA586UR7DPcqIlk+AhSLSHLOB75V4r69IJGo83KXxqfifN+5rzDbMe6p6qar+nfhXMZ5IpplZtCwAdojIKXj7FWouIk9gbv40jC9YtN7zB5KwbhdBzpJu6FS0y5bngRcw0Q+PRmgXaCnNiqHfgWQBw5zkjqs9yoiW7ZiZT1vgt3HIiff6ikSixmMb1VmE/b7nfxeQ/66hqu4j8ZEtIUlKZeZY8orCvD0Zsz+SjvFKjglVfU1ELsX45mQAn7iOp/WMYapaCCAiOWHa/MxxlkzDuFpEs2x5GXgRswf2H0xSv1AEWkpvwYNxQVXfFJGjMN/Vytrae0Gc9NaqOhlns15MplivxHV9RSKB4/EHjBJPw/+cY21F5HWMLukInKWqdWLRTBplJiIvUB0Lt58DqIj0VtUlmP2MzzFf3BnAX2M9l6p+TB2HZkTBeY6LimudC2Xd3aaqawBE5HHMhn1tfAnch7lhIznNeraUish9qvqMEwqT5XyGHExmD795DHhYRGZhZmeuYu8dixA/r68QshM9Hm9gZtqFmP1QP6MtTsF894nYj4uJpFFmwI+qOgrCmpdzgSWYZITrMBdEd3y42Oopbozn15ic/aHwEnpThMnekI8pgBGOmC2lARywUBhVdfO4naOqm0Ukg+gdiANJ5PWV6PH4HDPT+wmz1I4bx4UkHTPTS5SrSkzUe2XmWJ4aAzmOnxmY8J1zA9s5G49gfok3YabVmw5UP+uALhgXilswF1OoiIjA0JtoLbLHYXycZmMUWjimYxwkM4AyETlJVaMyMgSFwizCuDj8FP4IX3jJcYYuxcxi34nl4EReXwdgPLIwweu3O+fwg27ArzBxno0xe6c2BVAtfILZiL6D6tQ0kWIBn6E6u+qDCe9d3fEwMAvzq35r4BsicpOInKeqezA37zBMMY9ocLNhFBA5G8bvMLGOXTBm+SIRiXUPqTOm9Fk+JlVRInlPVfMdS+HlcchJ5PWVqPH4BGiJcSf53ieZ/TGFYZoCzVT1HVWt0/ut3iszx1JSiZl9VDp/kQK0/66qGzAxY4nYg6kXqOouVS11btDg4OGLVfXfItIP42ZxI9F7k7vZMH5L5MD+Cao6GPNLn6OqyzH7SbGwEeghIpfhf26wYNqLyFMi8m8gnsSHiby+EjUef8eEHA3B+Jz5wQRVfVZVR2MyrLgZOuqMer/MdDiN6CP/A7OrPhuhXVIjIs2dnO6hGOf8/xNwj6oWiEi0Vqxos2EMFJGfYTaVDxeR9sS+AfwpZqmSgU+l/EQkS1V3B7/uWAqPADJUNS/EodGSyOvL9/Fw+EBV3wEQEb/SFj3sZJgF4860HfPD1sUn+TGTFMosROR/ywht/yMi04A+VJd1S0V+j1MDUUSaqWpgMYlWIvI/TOGNz0XkLEwRlVeikHs15scjHXNjhQt9eRzooaqLHStcL0xK6VoRkaFAqap+LSJTMMu+1sQYAB6wn+qShlkCXhzQ5m3gn6r6GWaWOkBE3lCPFc0TcX35NR4ROF1M3YxKzH5zJCt1tFypqjU+v4gc54Ncz9R7Zeb8CrZwHoO5YNsSNA0XkYWYDLQTMS4IszGZUlN136yDiLyLCbrvhpm1AqCqfxKRF90sI6o6keh/MaPNCiHAsc5Str+qxlKpaDRwvoj0wFRdvw6TRibWHGOfYuJTr8EspSoxHvq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      "text/plain": "<Figure size 344.16x129.6 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4.78,1.8))\n",
    "sns.set_context(\"paper\", font_scale=1)\n",
    "barWidth=0.8\n",
    "\n",
    "temp.s = temp.s.round(1)\n",
    "temp.total = temp.total.astype(int)\n",
    "\n",
    "lrects = plt.bar(\n",
    "    r, \n",
    "    temp['s'], \n",
    "    color=sns.color_palette(\"Set2\")[6], \n",
    "    edgecolor='black', \n",
    "    width=barWidth, \n",
    "    label=\"Success\")\n",
    "hrects = plt.bar(\n",
    "    r, \n",
    "    100-temp['s'], \n",
    "    bottom=temp['s'], \n",
    "    color=sns.color_palette(\"Set2\")[7], \n",
    "    edgecolor='black', \n",
    "    width=barWidth, \n",
    "    label=\"Error\")\n",
    "\n",
    "plt.xticks(r, names, rotation=90) #, fontsize=15)\n",
    "plt.xlabel(\"Subject\") #, size=15)\n",
    "\n",
    "plt.legend(loc='best', ncol=1) #,prop={'size': 15})\n",
    "\n",
    "temp['year'] = temp.index\n",
    "temp = temp.reset_index(drop=True)\n",
    "\n",
    "plt.title(\"Re-execution rate per field of study\") #, size=20)\n",
    "\n",
    "autolabel(lrects, plt)\n",
    "autolabel_count(hrects, plt, temp)\n",
    "\n",
    "#plt.savefig(\"subject_new2.pdf\", bbox_inches = \"tight\", dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}